I have a problem I haven't been able to figure out-
Say I have an image of arbitrary dimensions. I resize it so that it fits inside 1024x768 while keeping the aspect ratio. I center it on the screen. After doing this, how can I find where the upper left corner will end up?
So, if the image is wider than it is tall, we end up with something like
The green rectangle started at a different size. It was resized to fit the pink rectangle. I want to find the upper left corner of the green rectangle.
I wrote a bunch of notes and drew a bunch of diagrams, but I'm getting all the wrong answers. Can someone explain how to do this? I'm using python 2.7
Let w,h be the size of your image.
To fit the width of 1024, we must scale the image by:
>>> r=1024./w
However, if the image is taller, after scaling it by r, its height won't fit the screen, so in this case the scaling factor is:
>>> if h*r > 768: r=768./h
The coordinate of the upper left corner of the scaled image is:
>>> (1024-w*r)*0.5,(768-h*r)*0.5
Edit:
A handy function to compute the topleft point (works in Python 2.x as well):
def topLeft(w,h,screenw=1024,screenh=768):
r=float(screenw)/float(w)
if h*r > screenh: r=float(screenh)/float(h)
return (screenw-w*r)*0.5,(screenh-h*r)*0.5
Related
I am trying to determine the circle parameters, using its slice, visible in the image. I want to know the radius, coordinates of center and draw the full circle (on the bigger picture of course). I've tried to use imfindcircles with Hough Transformation. Unfortunately, it looks like the slice of the circle is not big enough, and the algorithm does not recognize it as the full circle.
clear all;
close all;
figure();
image = imread('circle3.jpg');
imshow(image);
Rmin = 10;
Rmax = 10000;
[centersBright, radiiBright] = imfindcircles(image,[Rmin
Rmax],'ObjectPolarity','bright','Method','TwoStage','Sensitivity',0.98,'EdgeThreshold',0.9);
viscircles(centersBright, radiiBright,'Color','b');
I have changed the sensivity, threshold, Rmin, Rmax, even the method, but still, nothing happens. How can I detect this circle, if not in Matlab, then maybe in Python?
Hope you guys are doing well.
I have a question about opencv and extracting the same point from number of images.
Say, we have a dataset of around 100 images. (maybe more but for this purpose it will suffice).
Image will look something like:
As you can see in the image, there is an area marked in RED. I have marked that using Paint for this purpose. It indicates the highest point of the heap of soil. All the 100 images we have look more or less the same (without that crane in the background. But it can be removed using some opencv techniques, so that is not an issue). But this heap of soil can be either on the left hand side or the right hand side. According to its position, the coordinate of the highest point in the heap will change.
So, my question is, how to find this position given that the heap can be either on left or right side?
Note that this position can be relative to some object (for example in this image, midpoint of the crane) or if the images are of different size than we can resize the images to have same dimensions and take the coordinates of the point w.r.t the image itself.
How do we find out the highest point of the heap though? Should we manually go through each image, label that point and make a dataset with the images and bounding boxes? Or is there another decent soulution to this?
Also, if the soil heap is labelled manually (Like shading the required area i.e. heap of an image) using Paint or some other software, would that help too? But I cannot think of anything to do after this.
Thank You.
So I am sure this can be done in a better way than my answer, but here goes:
"Also, if the soil heap is labelled manually (Like shading the required area i.e. heap of an image) using Paint or some other software, would that help too? But I cannot think of anything to do after this."
In regards to that particular statement if you mark out the region of interest in a distinctive color and shape, like you have done in the above example. You can use opencv to detect that particular region of interest and its coordinates within the image.
I think the best solution is deep learning because detective always has different backgrounds. You can use Faster rcnn, or if you want speed, you can make nice detectives with a good training using Yolo algorithm. You can find Github repo easily. The mathematics of work is described in these links.
Faster RCNN https://arxiv.org/abs/1506.01497
Yolo https://pjreddie.com/darknet/yolo/
basically you can resize image. Keep aspect ratio!
def image_resize(image, width = None, height = None, inter = cv2.INTER_AREA):
dim = None
(h, w) = image.shape[:2]
if width is None and height is None:
return image
if width is None:
# calculate the ratio of the height and construct the
# dimensions
r = height / float(h)
dim = (int(w * r), height)
else:
# calculate the ratio of the width and construct the
# dimensions
r = width / float(w)
dim = (width, int(h * r))
# resize the image
resized = cv2.resize(image, dim, interpolation = inter)
# return the resized image
return resized
image = image_resize(image, height = ..What you want..)
I'm trying to find a way to transform an image by translating one of its vertexes.
I have already found various methods for transforming an image like rotation and scaling, but none of the methods involved skewing like so:
There is shearing, but it's not the same since it can move two or more of the image's vertex while I only want to move one.
What can I use that can perform such an operation?
I took your "cat-thing" and resized it to a nice size, added some perfectly vertical and horizontal white gridlines and added some extra canvas in red at the bottom to give myself room to transform it. That gave me this which is 400 pixels wide and 450 pixels tall:
I then used ImageMagick to do a "Bilinear Forward Transform" in Terminal. Basically you give it 4 pairs of points, the first pair is where the top-left corner is before the transform and then where it must move to. The next pair is where the top-right corner is originally followed by where it ends up. Then the bottom-right. Then the bottom-left. As you can see, 3 of the 4 pairs are unmoved - only the bottom-right corner moves. I also made the virtual pixel black so you can see where pixels were invented by the transform in black:
convert cat.png -matte -virtual-pixel black -interpolate Spline -distort BilinearForward '0,0 0,0 399,0 399,0 399,349 330,430 0,349 0,349' bilinear.png
I also did a "Perspective Transform" using the same transform coordinates:
convert cat.png -matte -virtual-pixel black -distort Perspective '0,0 0,0 399,0 399,0 399,349 330,430 0,349 0,349' perspective.png
Finally, to illustrate the difference, I made a flickering comparison between the 2 images so you can see the difference:
I am indebted to Anthony Thyssen for his excellent work here which I commend to you.
I understand you were looking for a Python solution and would point out that there is a Python binding to ImageMagick called Wand which you may like to use - here.
Note that I only used red and black to illustrate what is going on (atop the Stack Overflow white background) and where aspects of the result come from, you would obviously use white for both!
The perspective transformation is likely what you want, since it preserves straight lines at any angle. (The inverse bilinear only preserves horizontal and vertical straight lines).
Here is how to do it in ImageMagick, Python Wand (based upon ImageMagick) and Python OpenCV.
Input:
ImageMagick
(Note the +distort makes the output the needed size to hold the full result and is not restricted to the size of the input. Also the -virtual-pixel white sets color of the area outside the image pixels to white. The points are ordered clockwise from the top left in pairs as inx,iny outx,outy)
convert cat.png -virtual-pixel white +distort perspective \
"0,0 0,0 359,0 359,0 379,333 306,376 0,333 0,333" \
cat_perspective_im.png
Python Wand
(Note the best_fit=true makes the output the needed size to hold the full result and is not restricted to the size of the input.)
#!/bin/python3.7
from wand.image import Image
from wand.display import display
with Image(filename='cat.png') as img:
img.virtual_pixel = 'white'
img.distort('perspective', (0,0, 0,0, 359,0, 359,0, 379,333, 306,376, 0,333, 0,333), best_fit=True)
img.save(filename='cat_perspective_wand.png')
display(img)
Python OpenCV
#!/bin/python3.7
import cv2
import numpy as np
# Read source image.
img_src = cv2.imread('cat.png')
# Four corners of source image
# Coordinates are in x,y system with x horizontal to the right and y vertical downward
pts_src = np.float32([[0,0], [359,0], [379,333], [0,333]])
# Four corners of destination image.
pts_dst = np.float32([[0, 0], [359,0], [306,376], [0,333]])
# Get perspecive matrix if only 4 points
m = cv2.getPerspectiveTransform(pts_src,pts_dst)
# Warp source image to destination based on matrix
# size argument is width x height
# compute from max output coordinates
img_out = cv2.warpPerspective(img_src, m, (359+1,376+1), cv2.INTER_LINEAR, borderMode=cv2.BORDER_CONSTANT, borderValue=(255, 255, 255))
# Save output
cv2.imwrite('cat_perspective_opencv.png', img_out)
# Display result
cv2.imshow("Warped Source Image", img_out)
cv2.waitKey(0)
cv2.destroyAllWindows()
Is it possible the value pixel of image is change after image rotate? I rotate an image, ex, I rotate image 13 degree, so I pick a random pixel before the image rotate and say it X, then I brute force in image has been rotate, and I not found pixel value as same as X. so is it possible the value pixel can change after image rotate? I rotate with opencv library in python.
Any help would be appreciated.
Yes, it is possible for the initial pixel value not to be found in the transformed image.
To understand why this would happen, remember that pixels are not infinitely small dots, but they are rectangles with horizontal and vertical sides, with small but non-zero width and height.
After a 13 degrees rotation, these rectangles (which have constant color inside) will not have their sides horizontal and vertical anymore.
Therefore an approximation needs to be made in order to represent the rotated image using pixels of constant color, with sides horizontal and vertical.
If you just rotate the same image plane the image pixels will remain same. Simple maths
I would like to use Python (and the PIL library) to resize a picture (eventually with width > height or height > width) in a fixed size, let's say (144px * 144px).
I've already found topics about cropping the picture, but I would like to :
Find the biggest side of the picture
Do a thumbnail of the picture with biggest side = 144px and the other side = a ratio
Center the smallest side
Fill the borders of the smallest side with a transparent background so that the final picture is (144px * 144px)
Any ideas on how to do that? For the two first points I would use :
image = PIL.Image.open(pic)
image.thumbnail((144,144), PIL.Image.BILINEAR)
But then I'm blocked. Thanks!